Cropland abandonment in the context of drought, economic restructuring, and migration in northeast Syria
Bibliographic record
Abstract
Abstract Farmland abandonment is often associated with biophysical, political, or socio-economic changes, like droughts, economic reforms, rural-urban migration, or armed conflicts. Syria has seen several such changes in the period between 2000–2011, however, few assessments of how these factors have interacted with land abandonment have been carried out. In this study we investigate land abandonment patterns in northeast Syria, using a land use classification based on satellite data to indicate agricultural drought impacts and land abandonment. We combine these data with information on land use and migration patterns collected through a unique fieldwork, including surveys and interviews with Syrian farmers who had migrated to Turkey. Our analysis shows that drought coincided with a strong drop in cultivated croplands in 2008 and 2009. We also found a comparatively high cropland abandonment between 2001 and 2013, however no strong increases during or after drought years. Local insights indicate that migration took place during both normal years and drought years, and that most migrants had abandoned their lands after leaving Syria. We suggest that long-term mismanagement of water resources along with changes in the political economy, drove land abandonment in northeast Syria between 2001 and 2010. After 2011, armed conflict likely drove abandonment, but rates remained similar to the pre-conflict period. We discuss the potential of land abandonment as an indicator of rural migration in areas where migration data is sparse and conclude that more research is needed to understand the migration-land abandonment nexus, particularly in the Middle East.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".